Abstract

Traditionally, electrical power systems had formed the vertically integrated industry structures based on the economics of scale. However, power systems have been recently reformed to increase their energy efficiency. According to these trends, the Korean power industry underwent partial reorganization and competition in the generation market was initiated in 2001. In competitive electric markets, accurate load data is one of the most important issues to maintaining flexibility in the electric markets as well as reliability in the power systems. In practice, the measuring load data can be uncertain because of mechanical trouble, communication jamming, and other issues. To obtain reliable load data, an efficient evaluation technique to adjust the missing load data is required. This paper analyzes the load pattern of historical real data and then the tuned ARIMA (Autoregressive Integrated Moving Average), PCHIP (Piecewise Cubic Interpolation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and also tested against historical measured data from the Korea Energy Management Corporation (KEMCO).

주제어

#ARIMA (Autoregressive Integrated Moving Average)   #Load Forecast   #PCHIP (Piecewise Cubic Interpolation)   #Branch and Bound Algorithm   #Time Series Analysis  

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참고문헌 (16)

1. A.A. El-keib and X. Ma, 'Calculating Short-Run Marginal Costs of Active and Reactive Power Production', IEEE Trans. on Power Systems, Vol. 12, No. 2, May 1997, pp. 559-565 
2. Y.Z. Li and A.K. David, ‘Wheeling Rates of Reactive Power Flow under Marginal Cost Pricing’, IEEE Trans. on Power Systems, Vol. 9, No. 3, Aug. 1994, pp. 1263-1269 
3. M.C. Caramanis et al., ‘The Costs of Wheeling and Optimal Wheeling Rates’, IEEE Trans. on Power Systems, Vol. 1, No. 1, Feb. 1986, pp. 63-73 
4. K. Ro and S. Park, ‘A Method to Calculate Charge for Reactive Power Service under Competition of Electric Power Utilities’, KIEE International Trans. on Power Engineering, Vol. 11A, No. 4, December 2001, pp. 39-44 
5. J.W. Lamont and J. Fu, ‘Cost Analysis of Reactive Power Support’, IEEE Trans. on Power Systems, Vol. 14, No. 3, August 1999, pp. 890-898 
6. J.B. Gil et al., ‘Reactive Power Pricing: A Conceptual Framework for Remuneration and Charging Procedures’, IEEE Trans. on Power Systems, Vol. 15, No. 2, May 2000, pp. 483-489 
7. S. Ahmed and G. Strbac, ‘A Method for Simulation and Analysis of Reactive Power Market’, IEEE Trans. on Power Systems, Vol. 15, No. 3, August 2000, pp. 1047-1052 
8. Korea Power Exchange, Ancillary Services Agreement- Technical Schedules, September 2002 
9. National Grid Company, NGC reactive market report, May 2000 
10. J.Y. Choi et al., ‘Optimal Real Time Pricing of Real and Reactive Powers’, IEEE Trans. on Power Systems, Vol.13, No. 4, Nov. 1998, pp. 1226-1231 
11. S. Hao and A. Papalexopoulos, ‘Reactive Power Pricing and Management’, IEEE Trans. on Power Systems, Vol.12, No. 1, February 1997, pp. 95-104 
12. N.H. Dandachi et al., ‘OPF for Reactive Pricing Studies on the NGC System’, IEEE Trans. on Power Systems, Vol.11, No. 1, February 1996, pp. 226-232 
13. New York Independent System Operator Ancillary Services Manual, 1999 
14. California Independent System Operator Corporation, Ancillary services requirement protocol, FERC Electricity Tariff, October 2000 
15. National Grid Company, An introduction to reactive power: Ancillary Services, June 1998. 
16. M.L. Baughman and S.N. Siddiqi, ‘Real Time Pricing of Reactive Power: Theory and Case Study Results’, IEEE Trans. on Power Systems, Vol. 6, No. 1, Feb. 1991, pp. 23-29 

이 논문을 인용한 문헌 (1)

1. Kim Dae-Yong ; Lee Chan-Joo ; Jeong Yun-Won ; Park Jong-Bae ; Shin Joong-Rin 2006. "Development of System Marginal Price Forecasting Method Using ARIMA Model" 전기학회논문지. The transactions of the Korean Institute of Electrical Engineers. A / A, 전력기술부문, 55(2): 85~93  원문보기 상세보기